89 AI use cases • Executive briefs • Technical analysis
This is like giving a car maker’s supply chain a super-smart co-pilot that constantly watches demand, inventory, and supplier risks, and then suggests better plans and quick course-corrections before problems show up on the road.
This is like giving a car or engine a brain that learns to “listen” to its own sensors and predict how much life it has left before something fails. Instead of engineers handcrafting dozens of rules and features, the model learns directly from raw sensor data when parts will wear out.
Think of a polygenic risk score as a “credit score for heart disease” built from thousands of tiny changes in your DNA. This paper reviews how AI can act like a smarter credit bureau—sifting through massive genomic and clinical datasets to build more accurate and personalized scores that predict who is at high risk of heart problems, long before symptoms start.
Think of this as a super-smart co‑driver made of many small AI helpers that can not only see the road and steer, but also plan trips, talk to other systems (like traffic lights or charging stations), and make complex decisions on its own to keep passengers safe and moving efficiently.
Think of this as a ‘medical weather forecast’ system powered by AI: it looks at a huge mix of patient data (labs, scans, genetics, history) to predict who is likely to get which disease and which treatment is most likely to work for each person.
This is like giving your car factory a super-smart assistant that watches everything on the line, spots problems before they happen, and suggests small tweaks that make the whole plant run faster, cheaper, and with fewer defects.
Think of Orbitae AI as a smart control tower for an automotive company’s data. It connects to all your scattered data sources (production, sales, after‑sales, supply chain), lets managers ask questions in natural language, and then turns complex analytics into simple dashboards, forecasts, and recommendations to run the business better and faster.
This is like a smart GPS and financial advisor for car parts moving around the world: it watches shipping routes, tariffs, and costs in real time and then suggests better ways to move parts so automakers avoid delays and surprise expenses when trade rules change.
This is like a massive safety report card for modern car safety features (like automatic braking and lane-keeping). It uses real crash data to figure out which features actually reduce injuries, by how much, and in what situations.
This is like giving your supply chain a smart GPS and weather system that constantly looks ahead, finds the fastest and safest routes for parts and materials, and automatically reroutes when there’s a disruption (factory shutdown, port delay, raw‑material shortage).
Modern cars are turning into rolling AI supercomputers. A single powerful computer in the car will handle self-driving assistance, watch the driver and passengers for safety, manage infotainment, and stay always-connected to the cloud—replacing dozens of small, separate control boxes with one central brain.
This is about using machine learning as a smart ‘check engine’ light for factories and vehicles. Instead of waiting for a part to fail or doing maintenance on a fixed calendar, models watch sensor data (vibration, temperature, voltage, etc.) and warn you ahead of time when something is likely to break so you can fix it before it causes downtime.
This is like giving a car factory an always‑on air-traffic controller that watches every step of production in real time, finds bottlenecks and waste, and then suggests the fastest, cheapest way to keep parts and cars moving.
Think of a modern car as a smartphone on wheels: most of the innovation comes from software and AI, not just the engine. Instead of buying a fixed-function machine, you get a computer platform where new driving features, safety functions, and in‑car experiences can be added or upgraded over time—much like installing apps or over‑the‑air updates on your phone.
Think of this as a GPS and autopilot for your purchasing department. Instead of buyers manually chasing quotes, checking hundreds of suppliers, and reacting late to price or risk changes, the system continuously scans data, predicts issues, and recommends the best sourcing moves—who to buy from, when, and at what terms.
This is like putting a smart mechanic’s brain inside your machines. Sensors listen to vibrations, temperatures, sounds, etc., and AI learns what “healthy” looks like versus “about to break.” It then flags early signs of failure so you can fix parts before they actually break.
Imagine every car and truck constantly sending little health check signals to the cloud, where an AI mechanic listens and warns you *before* something breaks. That’s predictive maintenance for vehicles.
This is like giving your car factory’s production line a smart “nervous system” and brain: sensors continuously watch machines and products, and an AI model predicts in real time what should be happening; a Kalman filter then cleans up noisy signals so the system can quickly detect when something is drifting off-spec and alert operators before it becomes a costly defect or breakdown.
Think of this as a digital crash-test and driving range for self-driving cars, where AI watches millions of miles of test drives, spots problems automatically, and organizes all the data so engineers can improve safety much faster.
This is like putting a smart stethoscope on an electric motor that listens to it while it runs and instantly tells you if something is starting to go wrong inside, before it breaks down.
Think of the automotive supply chain as a huge multi‑country relay race where parts are passed from one supplier to another until a finished car rolls off the line. AI is like a smart coach that watches the whole race in real time, predicts where delays will happen, and tells each runner how to adjust so the baton never gets dropped.
This is like having a smart inspector that watches all the process data from your production line and learns which patterns usually lead to costly defects or failures. Instead of just predicting “right vs wrong,” it focuses on the money: it prefers to catch the errors that are most expensive for you if they slip through, even if that means being a bit more permissive on low-cost issues.
Think of this as a very smart ‘air traffic controller’ for a car dealer’s lot. Instead of people guessing which cars to order, how many, and when, an AI looks at history, local demand, market prices, and OEM pipelines to tell dealers exactly what mix of vehicles they should stock and how to move them faster.
Think of this as a playbook that explains how the “brain” inside self-driving cars and advanced driver-assistance features works and how to design it safely. It’s not a single app, but a guide to building the AI that helps cars perceive the road, make driving decisions, and assist or replace human drivers.
This is a market research report that acts like a detailed weather forecast for self-driving cars worldwide until 2030—showing where, how fast, and in which segments autonomous vehicles are likely to grow.
This work is like testing two different "crystal balls" for car data: one based on classic math waves (Fourier series) and one based on modern neural networks (deep learning) to see which predicts complex automotive signals better and when.
Think of this as turning today’s car from a cautious helper into a near co‑pilot that can see, understand, and react to the road using AI—step by step moving from lane-keeping and automatic braking toward full self-driving.
Think of ADAS as a co‑pilot made of sensors and software that constantly watches the road and helps the driver stay safe—warning about danger, keeping the car in its lane, and even braking or steering in emergencies.
Think of this as an AI co‑pilot that constantly checks the car’s critical systems, looking for early warning signs of failures so that engineers can fix issues before they become safety problems.
This is a government-backed R&D program that helps companies use AI to move goods, parts, and vehicles more efficiently—like giving your supply chain a GPS and autopilot that constantly looks for faster, cheaper, and more reliable ways to deliver.
This is like giving an auto manufacturer a smart GPS for its supply chain that suggests the best routes not only by cost and speed, but also by how green and responsible each option is – using data instead of gut feel.
This is like giving your wiring-harness design team a very smart co-pilot that suggests optimal wire routes, sizes, and layouts automatically, instead of engineers doing every calculation and layout step by hand.
Imagine your car-parts supply chain as a highway system. A pandemic is like sudden roadblocks and accidents everywhere. This research looks at how AI can act like a smart traffic control center—constantly watching conditions, rerouting shipments, predicting future blockages, and suggesting backup routes and suppliers so parts still arrive on time.
This is like giving every machine in your factory a smart ‘check engine’ light that warns you days or weeks before something is about to break, so you can fix it at a convenient time instead of shutting the whole line down unexpectedly.
Think of a modern car tuner as a very smart mechanic’s assistant that has watched thousands of engine setups and road tests. Instead of a human slowly tweaking fuel, ignition, and turbo settings by trial and error, AI looks at huge amounts of sensor data, learns what combinations give the best power, efficiency, and reliability, then proposes or applies the optimal tune automatically for each specific car and driving style.
This is about turning cars into very smart robots on wheels that can drive themselves by using lots of cameras, sensors, and AI ‘brains’ built by tech companies in Silicon Valley.
This is like giving every car or factory machine its own digital doctor that constantly listens to its heartbeat and vibrations, learns what “healthy” looks like, and warns you before something breaks instead of after it fails.
This is about using smart algorithms as a ‘digital brain’ on the factory floor so machines can spot defects, predict breakdowns, and optimize production flows without a human watching every step.
Think of this as the car industry’s official playbook for how driving is becoming more automated and safer over time—from basic cruise control to cars that can help steer, brake, and avoid crashes on their own.
Think of this as a super-analyst that constantly watches your entire auto supply network – suppliers, logistics, and risks – and summarizes what’s happening and what might break, long before your planners could find it in spreadsheets and emails.
This is Nvidia selling the “brains and nervous system” for future cars. Instead of each carmaker building all the self‑driving and in‑car AI from scratch, they buy Nvidia’s computing hardware and software platform, plug in their own features and data, and get a ready‑made AI stack for autonomous and smart vehicles.
This is like a “health meter” for critical car or vehicle parts that uses past data and smart algorithms to predict how much life is left before they fail—so you can fix or replace them before they break.
This is about whether car makers should build their own self‑driving system from scratch or buy most of it from a specialist like buying an engine instead of inventing a new one. In practice, they usually mix both: they buy a proven ‘autonomy brain’ and then customize parts so it fits tightly with their cars and brand.
This is like putting a smart co-pilot in every commercial vehicle: it watches the road, the driver, and the surrounding traffic in real time, predicts when something risky is about to happen, and warns the driver so they can avoid a crash.
This is about using smart software that can learn patterns to keep car parts and finished vehicles flowing smoothly—from raw materials to dealerships—so the right parts arrive at the right place and time with less waste and fewer delays.
This is like giving your entire logistics network a nervous system and a brain: sensors (IoT) constantly tell you where every vehicle, part, and package is and how it’s doing, while AI decides the best routes, loading plans, and schedules to cut delays and waste.
This is like giving every child with cancer a personal scientific detective. Doctors test how that child’s own cancer cells react to many different drugs in the lab, then AI sifts through all the results plus medical data to recommend which treatments are most likely to work for that specific child, instead of relying only on one-size-fits-all protocols.
Think of it as giving your production line millions of tireless, ultra-precise eyes that watch every car part being built and flag problems instantly—far faster and more accurately than human inspectors.
This is like putting a smart “check engine” light on every critical machine in your operation. Instead of waiting for something to break, sensors and analytics constantly watch how equipment behaves and warn you early so you can fix small issues before they become big, expensive failures.
Think of cars that can drive themselves like a 24/7 chauffeur who never gets tired or distracted and talks constantly to other cars, traffic lights, and roads to move people and goods safely and efficiently.
This is like putting a super-smart co-pilot in your car that constantly looks at the road, listens, feels how the car is moving, and then decides when to steer, brake, or accelerate to drive itself safely.
Think of Level 4 self-driving as a very capable chauffeur that can handle nearly all driving in specific areas without your help. AI is the chauffeur’s brain and eyes: it constantly watches the road with cameras, radar and lidar, understands what’s happening, predicts what other drivers and pedestrians will do, and then controls the steering, braking, and acceleration to drive safely on its own.
This is like giving a car an extra pair of smart eyes and a fast brain so it can see the road, recognize dangers (cars, pedestrians, lanes, signs), and react quickly and safely. The paper reviews how camera-based vision and mathematical optimization are combined to make these assistance features more accurate and reliable.
This is like giving your electric car a very smart ‘early warning’ doctor that uses a new kind of computing (quantum computers) to spot battery problems before they become serious. Instead of waiting for a fault light to turn on, it learns subtle patterns in battery data and flags issues much earlier so you can fix them before they cause breakdowns.
This is like giving your supply chain analysts a supercharged research assistant that understands a map of all your suppliers, plants, parts, and shipments. It doesn’t just read documents; it also knows how everything is connected, so it can answer questions like “what breaks if this supplier fails?” instead of just keyword-searching through PDFs.
This is like having an extremely smart planning assistant for a fuel-cell hybrid electric vehicle (FCHEV). It simultaneously decides how big each powertrain component should be (fuel cell, battery, etc.) and how the vehicle should use them in real driving, using AI and realistic traffic patterns, to get the best trade-off between cost, efficiency, performance, and durability.
Think of a self-driving car as a very careful robot driver with superhuman eyesight and lightning-fast reflexes. Cameras, radar, and other sensors see the road, while AI is the brain that understands what’s happening, decides what to do, and steers, accelerates, or brakes to get you where you’re going safely.
Think of this as a co‑pilot in your car that’s always watching the road and your surroundings, warning you if something’s wrong and sometimes gently correcting your steering or speed to avoid accidents.
This is the car’s “brain and eyes” working together—using AI to watch the road, understand what’s happening, and help drive or even drive itself more safely than a distracted human.
Think of ADAS as a bundle of smart copilots in your car: one watches lane markings, another looks for pedestrians, another keeps safe distance from the car ahead, and all of them constantly nudge or override the driver to prevent accidents.
This is like giving a car very sharp eyes that can spot and understand all the painted lines and symbols on the road (lane lines, arrows, crosswalks) so it can stay in the correct lane and follow road rules automatically.
Think of ADAS as a very alert co‑pilot in your car. It constantly watches the road, other vehicles, pedestrians, and lane markings using cameras and sensors, then gently corrects your driving—braking, steering, or warning you—before something bad happens.
This is about using AI as an extra pair of eyes and a reflex system in the car that never gets tired—helping the driver stay in lane, avoid collisions, see blind spots, and react faster than a human can.
Think of ADAS as a co‑pilot inside your car: cameras, radar, and software continuously watch the road, warn the driver, and, when needed, subtly take control of steering, braking, or acceleration to avoid crashes and make driving easier.
Think of this as a special research focus on making the AI co‑pilot in modern cars safer and harder to hack. It’s about the brains behind lane-keeping, automatic braking, and self-driving features—how to ensure they don’t make dangerous mistakes and can’t be easily manipulated.
Think of this as a smart co‑pilot for ICU doctors when they prescribe and adjust blood thinner doses. It continuously learns from past patients and current lab results, then suggests the next best dosing decision—not just predicting what will happen, but recommending what to do.
This is like a smart co-pilot for planning and operations in automotive systems that constantly learns from data and uncertainty (traffic, failures, demand swings) and then optimizes decisions (routes, loads, schedules, configurations) so the system keeps working well even when things go wrong.
This is like an extremely focused self-driving feature: a smart cruise control that automatically keeps your car at a safe distance, spots possible collisions, and can limit speed so the car doesn’t go faster than is safe or allowed.
Think of AdaDrive as a smart co-pilot brain for self-driving cars that can ‘think slowly’ when it needs deeper reasoning and ‘react quickly’ when the situation is simple. It also understands human instructions and descriptions in natural language, so you can tell the car what to do in words and it can align its driving behavior accordingly.
This is about car makers and their suppliers moving their IT and engineering work into the cloud and layering AI on top so they can design cars faster, run factories more efficiently, and manage vehicles and customers more intelligently.
Think of CarDreamer as a driving simulator that lives inside an AI’s brain. Instead of only reacting to what cameras and sensors see right now, the AI learns an internal “world model” so it can imagine what will happen next, test different maneuvers in its head, and then choose the safest, smoothest action in the real car.
Think of an autonomous car that doesn’t rely on one ‘brain’ but on a panel of specialized mini-experts: one expert for highway lanes, one for intersections, one for emergency maneuvers, etc. A top-level controller decides, in real time, which expert (or combination of experts) should be in charge. ExpertAD is a research system that applies this ‘mixture of experts’ idea to make self-driving decisions more accurate and robust.
Think of this as a standardized obstacle course and scorecard for a self‑driving car’s “eyes and brain.” It systematically throws different road hazards at the car’s perception system (cameras, lidar, radar, and the AI that interprets them) to see what it notices, what it misses, and how often it makes dangerous mistakes.
This is like giving a car a pair of smart glasses that can instantly recognize what’s on the road (lanes, cars, pedestrians, signs) and clean up poor visibility (rain, fog, low light) so the driving computer sees a clearer, more understandable picture in real time.
Think of this as a science fair for car-related AI: a curated set of demos showing how AI can make vehicles safer, more reliable, and easier to build and operate. It’s less a single product and more a showcase of what’s now possible with AI across the automotive value chain.
This is a textbook-style introduction to self-driving cars: it explains the concepts, building blocks, and methods used to make vehicles perceive their surroundings, make decisions, and drive themselves safely.
This is a book that acts like a roadmap for car makers and tech leaders who want to build self-driving vehicles. It explains how to get from today’s driver-assistance features to fully autonomous driving—covering technology, safety, regulation, and business models rather than providing a single software product.
This whitepaper describes how new digital technologies – cloud, 5G/IoT connectivity, AI and data platforms – are reshaping how cars are designed, built, sold, and serviced. Think of it as a blueprint for turning a traditional car company into a connected software-and-services business.
Think of modern electric vehicles as smartphones on wheels: this article is a tour of the main ways AI is being used inside those vehicles and the surrounding ecosystem, from smarter driving to better battery management and charging.
This is a market research report that maps out how AI will be used in cars and the auto industry—things like self‑driving, driver assistance, in‑car assistants, predictive maintenance, and smarter manufacturing—along with how big the opportunity will be from 2025 to 2030.
This is about using AI as a smart co‑pilot and mechanic for cars and automotive operations—helping vehicles drive more safely, predict problems before they happen, and optimize performance and efficiency across the whole automotive ecosystem.
This is like an online newspaper focused only on self-driving and autonomous cars and trucks, collecting news and updates about the technology and the companies building it.
This is described as an AI system for logistics in the automotive space, likely acting like a smart dispatcher and planner that helps move vehicles, parts, or deliveries more efficiently by learning from routes, demand, and operations data.
Think of modern cars and car companies as having a very smart digital co‑pilot: AI helps design the car faster, choose the right features and prices, run factories more efficiently, and make driving safer and more personalized for the owner.
Think of this as a research snapshot about the smart features in cars that help drivers stay safe—like automatic braking and lane-keeping—not as a specific AI app or software product. It’s more of a market overview than a tool you can deploy.
Think of a modern car like a smartphone on wheels: most of its features – from how it drives to how it entertains you – are controlled by software that can be updated over the air, instead of being fixed the day it leaves the factory.
Think of this as a reference hub that explains and organizes modern driver-assistance features in cars (like lane-keeping and automatic emergency braking), helping the industry align on what they are, how they work, and why they matter for safety.
This is essentially a market and technology overview of the driver‑assist features you see in modern cars—like lane keeping, automatic braking, and adaptive cruise control—focused on how they will evolve in North America by 2026.
Think of AI in Canada’s auto industry as a smart control tower overseeing cars, factories, and supply chains. It helps cars drive themselves, factories fix machines before they break, and companies build and sell the right vehicles at the right time.